
import pandas as pd 

# how many samples do we have? -- We have 698 samples

# how many features do we have? -- We have 11 features

# what are the classes -- M and B

# how many classes do we have? -- We have 2 classes

# how many samples of each class? are they balanced?

# B    357
# M    212

# The dataset is not balanced. B accounts for 62.74% of the dataset, and M accounts for 37.26%

# read the dataset
df_cancer = pd.read_csv("cancer.csv")
print(df_cancer)

           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \
0      842302         M        17.99         10.38          122.80     1001.0   
1      842517         M        20.57         17.77          132.90     1326.0   
2    84300903         M        19.69         21.25          130.00     1203.0   
3    84348301         M        11.42         20.38           77.58      386.1   
4    84358402         M        20.29         14.34          135.10     1297.0   
..        ...       ...          ...           ...             ...        ...   
564    926424         M        21.56         22.39          142.00     1479.0   
565    926682         M        20.13         28.25          131.20     1261.0   
566    926954         M        16.60         28.08          108.30      858.1   
567    927241         M        20.60         29.33          140.10     1265.0   
568     92751         B         7.76         24.54           47.92      181.0   

     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \
0            0.11840           0.27760         0.30010              0.14710   
1            0.08474           0.07864         0.08690              0.07017   
2            0.10960           0.15990         0.19740              0.12790   
3            0.14250           0.28390         0.24140              0.10520   
4            0.10030           0.13280         0.19800              0.10430   
..               ...               ...             ...                  ...   
564          0.11100           0.11590         0.24390              0.13890   
565          0.09780           0.10340         0.14400              0.09791   
566          0.08455           0.10230         0.09251              0.05302   
567          0.11780           0.27700         0.35140              0.15200   
568          0.05263           0.04362         0.00000              0.00000   

     symmetry_mean  fractal_dimension_mean  
0           0.2419                 0.07871  
1           0.1812                 0.05667  
2           0.2069                 0.05999  
3           0.2597                 0.09744  
4           0.1809                 0.05883  
..             ...                     ...  
564         0.1726                 0.05623  
565         0.1752                 0.05533  
566         0.1590                 0.05648  
567         0.2397                 0.07016  
568         0.1587                 0.05884  

[569 rows x 12 columns]


print(df_cancer.info())

# what are the names of the features

# id, radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_mean, compactness_mean
# concavity_mean, concave points_mean, symmetry_mean, fractal_dimension_mean

# what are the types of features?
# int64, Object, float64

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 12 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   id                      569 non-null    int64  
 1   diagnosis               569 non-null    object 
 2   radius_mean             569 non-null    float64
 3   texture_mean            569 non-null    float64
 4   perimeter_mean          569 non-null    float64
 5   area_mean               569 non-null    float64
 6   smoothness_mean         569 non-null    float64
 7   compactness_mean        569 non-null    float64
 8   concavity_mean          569 non-null    float64
 9   concave points_mean     569 non-null    float64
 10  symmetry_mean           569 non-null    float64
 11  fractal_dimension_mean  569 non-null    float64
dtypes: float64(10), int64(1), object(1)
memory usage: 53.5+ KB
None


classes = pd.unique(df_cancer.iloc[:, 1])
num_classes = len(classes)
each_class = df_cancer.iloc[:, 1].value_counts()

print(classes)
print(num_classes)
print(each_class)

['M' 'B']
2
B    357
M    212
Name: diagnosis, dtype: int64


# change categorical values of variety to numeric values

from sklearn.preprocessing import LabelEncoder 
labelencoder= LabelEncoder() #initializing an object of class LabelEncoder
df_cancer['diagnosis'] = labelencoder.fit_transform(df_cancer['diagnosis']) #fitting and transforming the desired categorical column.

print(df_cancer['diagnosis'].unique())

df_cancer

[1 0]


# Drop the id column since we won't be needing it
df_cancer = df_cancer.drop('id', axis=1)

df_cancer


# are there any missing values?
# if so, what are you going to do about the missing values?

# There are no missing values in our dataset


df_cancer.isna().apply(pd.value_counts)


from matplotlib import pyplot as plt

df_cancer.boxplot(figsize = (15,6))

#plt.savefig('plots/i0.png')
plt.show()
df_cancer.describe().T


# Wrap IQR check into a function for easier later usage
def check_outliers(dataframe):
    Q1 = df_cancer.quantile(0.25)
    Q3 = df_cancer.quantile(0.75)
    IQR = Q3 - Q1

    # let's see how many outliers are in every column
    df2 = ((df_cancer < (Q1 - 1.5 * IQR)).sum()) 
    df3 = ((df_cancer > (Q3 + 1.5 * IQR)).sum())
    print(df2)
    print(df3)
    
check_outliers(df_cancer)

diagnosis                 0
radius_mean               0
texture_mean              0
perimeter_mean            0
area_mean                 0
smoothness_mean           1
compactness_mean          0
concavity_mean            0
concave points_mean       0
symmetry_mean             1
fractal_dimension_mean    0
dtype: int64
diagnosis                  0
radius_mean               14
texture_mean               7
perimeter_mean            13
area_mean                 25
smoothness_mean            5
compactness_mean          16
concavity_mean            18
concave points_mean       10
symmetry_mean             14
fractal_dimension_mean    15
dtype: int64


# Wrap IQR method into a function for easier later usage
def handle_outliers(df, column_name):
    q1 = df[column_name].quantile(0.25)
    q3 = df[column_name].quantile(0.75)
    iqr = q3 - q1
    
    # print(f'q1 of {column_name}: {q1}')
    # print(f'q3 of {column_name}: {q3}')
    
    df.loc[(df[column_name] < (q1 - 1.5 * iqr)), column_name] = (q1 - 1.5 * iqr)
    df.loc[(df[column_name] > (q3 + 1.5 * iqr)), column_name] = (q3 + 1.5 * iqr)
    
    return df


handle_outliers(df_cancer, 'radius_mean')
handle_outliers(df_cancer, 'texture_mean')
handle_outliers(df_cancer, 'perimeter_mean')
handle_outliers(df_cancer, 'area_mean')
handle_outliers(df_cancer, 'smoothness_mean')
handle_outliers(df_cancer, 'compactness_mean')
handle_outliers(df_cancer, 'concavity_mean')
handle_outliers(df_cancer, 'symmetry_mean')
handle_outliers(df_cancer, 'concave points_mean')
handle_outliers(df_cancer, 'fractal_dimension_mean')


# Recheck the dataframe for outliers
df_cancer.boxplot(figsize = (15,6))

plt.show()
df_cancer.describe().T

check_outliers(df_cancer)

diagnosis                 0
radius_mean               0
texture_mean              0
perimeter_mean            0
area_mean                 0
smoothness_mean           0
compactness_mean          0
concavity_mean            0
concave points_mean       0
symmetry_mean             0
fractal_dimension_mean    0
dtype: int64
diagnosis                 0
radius_mean               0
texture_mean              0
perimeter_mean            0
area_mean                 0
smoothness_mean           0
compactness_mean          0
concavity_mean            0
concave points_mean       0
symmetry_mean             0
fractal_dimension_mean    0
dtype: int64


# what kind of distributions do the features have?

plt.figure(figsize= (20,10))
plt.subplot(2, 5, 1)
plt.hist(df_cancer['radius_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('radius_mean')

plt.subplot(2, 5, 2)
plt.hist(df_cancer['texture_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('texture_mean')

plt.subplot(2,5,3)
plt.hist(df_cancer['perimeter_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('perimeter_mean')

plt.subplot(2,5,4)
plt.hist(df_cancer['area_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('area_mean')

plt.subplot(2,5,5)
plt.hist(df_cancer['smoothness_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('smoothness_mean')

plt.subplot(2,5,6)
plt.hist(df_cancer['compactness_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('compactness_mean')

plt.subplot(2,5,7)
plt.hist(df_cancer['concavity_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('concavity_mean')

plt.subplot(2,5,8)
plt.hist(df_cancer['concave points_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('concave points_mean')

plt.subplot(2,5,9)
plt.hist(df_cancer['symmetry_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('symmetry_mean')

plt.subplot(2,5,10)
plt.hist(df_cancer['fractal_dimension_mean'], color='lightblue', edgecolor = 'black', alpha = 0.7)
plt.xlabel('fractal_dimension_mean')

plt.show()


import seaborn as sns

# what kind of correlation does the data exhibit?

sns.pairplot(df_cancer, hue='diagnosis')  #pairplot
#plt.savefig('plots/pairplot.png')
plt.show()


cancer_correlation = df_cancer.corr()
display(cancer_correlation)


plt.figure(figsize=(12, 10))
sns.heatmap(cancer_correlation, annot=True)
plt.show()


from IPython.display import Image
%matplotlib inline


# define plot_decision_regions for later use


from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt

# To check recent matplotlib compatibility
import matplotlib
from distutils.version import LooseVersion


def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):

    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')
    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
    cmap = ListedColormap(colors[:len(np.unique(y))])

    # plot the decision surface
    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                           np.arange(x2_min, x2_max, resolution))
    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
    Z = Z.reshape(xx1.shape)
    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)
    plt.xlim(xx1.min(), xx1.max())
    plt.ylim(xx2.min(), xx2.max())

    for idx, cl in enumerate(np.unique(y)):
        plt.scatter(x=X[y == cl, 0], 
                    y=X[y == cl, 1],
                    alpha=0.8, 
                    color=colors[idx],
                    marker=markers[idx], 
                    label=cl, 
                    edgecolor='black')

    # highlight test examples
    if test_idx:
        # plot all examples
        X_test, y_test = X[test_idx, :], y[test_idx]

        
        if LooseVersion(matplotlib.__version__) < LooseVersion('0.3.4'):
            plt.scatter(X_test[:, 0],
                        X_test[:, 1],
                        c='',
                        edgecolor='black',
                        alpha=1.0,
                        linewidth=1,
                        marker='o',
                        s=100, 
                        label='test set')
        else:
            plt.scatter(X_test[:, 0],
                        X_test[:, 1],
                        c='none',
                        edgecolor='black',
                        alpha=1.0,
                        linewidth=1,
                        marker='o',
                        s=100, 
                        label='test set')


import numpy as np


# X = df_cancer.iloc[:, [1, 4]] # area_mean and radius_mean -- 0.889 Accuracy score
# X = df_cancer.iloc[:, [6, 8]] # compactness_mean & concave points_mean -- 0.924 Accuracy score 
# X = df_cancer.iloc[:, [3, 4, 6, 8]] # perimeter_mean & concave points_mean -- 0.9415204678362573
# X = df_cancer.iloc[:, [1, 3, 4, 6, 8]] 5 dimensions -- 0.9473684210526315
X = df_cancer.iloc[:, [3, 8]] # perimeter_mean & concave points_mean -- 0.936 Accuracy score 
y = df_cancer.iloc[:, 0]

# print(X)
print(y)
print(X.shape)
print(y.shape)

0      1
1      1
2      1
3      1
4      1
      ..
564    1
565    1
566    1
567    1
568    0
Name: diagnosis, Length: 569, dtype: int64
(569, 2)
(569,)


y = pd.factorize(df_cancer.iloc[:, 0])[0]
print(y.shape)
print(y)

(569,)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0
 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1
 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1
 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0
 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1
 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1
 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1
 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0
 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1
 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1
 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1
 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0
 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 0 0 0 0 0 0 1]


# 2. Split data into a training set and test set
#    Splitting data into 70% training and 30% test data:


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=1, stratify=y)


print('Labels count in y:', np.bincount(y))
print('Labels count in y_train:', np.bincount(y_train))
print('Labels count in y_test:', np.bincount(y_test))

print(X_train.shape)
print(X_test.shape)
print(X_train)

Labels count in y: [212 357]
Labels count in y_train: [148 250]
Labels count in y_test: [ 64 107]
(398, 2)
(171, 2)
     perimeter_mean  concave points_mean
227           97.45              0.03780
104           67.41              0.01201
248           68.01              0.01615
356           85.09              0.05603
90            94.57              0.02957
..              ...                  ...
479          109.80              0.09194
509          103.80              0.08520
120           73.34              0.02623
235           89.79              0.01896
161          126.30              0.09667

[398 rows x 2 columns]


from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer

#scaler = StandardScaler()

#X_train_std = scaler.fit_transform(X_train)

#X_test_std = scaler.transform(X_test)


scaler= Normalizer().fit(X_train) # the scaler is fitted to the training set
X_train_normal= scaler.transform(X_train) # the scaler is applied to the training set
X_test_normal = scaler.transform(X_test) # the scaler is applied to the test set


from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=5, 
                           p=2, 
                           metric='minkowski')

# knn=KNeighborsClassifier(3)
# knn.fit(X_train_normal, y_train)
# y_pred_sklearn= knn.predict(X_test)
# print(y_pred_sklearn)


# 4. Build the model (.fit)


from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer

scaler = StandardScaler()

X_train_std = scaler.fit_transform(X_train)

X_test_std = scaler.transform(X_test)


#scaler= Normalizer().fit(X_train) # the scaler is fitted to the training set
#X_train_normal= scaler.transform(X_train) # the scaler is applied to the training set
#X_test_normal = scaler.transform(X_test) # the scaler is applied to the test set


knn.fit(X_train_std, y_train)

X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

plot_decision_regions(X_combined_std, y_combined, 
                      classifier=knn, test_idx=range(105, 150))

plt.xlabel('perimeter_mean')
plt.ylabel('Radius Mean')
plt.legend(loc='upper left')
plt.tight_layout()
#plt.savefig('images/03_24.png', dpi=300)
# plt.show()

/tmp/ipykernel_247/2412832179.py:28: UserWarning: You passed a edgecolor/edgecolors ('black') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.
  plt.scatter(x=X[y == cl, 0],
/tmp/ipykernel_247/2412832179.py:42: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  if LooseVersion(matplotlib.__version__) < LooseVersion('0.3.4'):


# 5. Apply the model to the test data (.predict)
# 6. Estimate performance (accuracy score)


from sklearn import metrics 
y_pred = knn.predict(X_test_std)
print(metrics.accuracy_score(y_test, y_pred))

0.935672514619883

	count	mean	std	min	25%	50%	75%	max
diagnosis	569.0	0.372583	0.483918	0.00000	0.00000	0.00000	1.00000	1.00000
radius_mean	569.0	14.127292	3.524049	6.98100	11.70000	13.37000	15.78000	28.11000
texture_mean	569.0	19.289649	4.301036	9.71000	16.17000	18.84000	21.80000	39.28000
perimeter_mean	569.0	91.969033	24.298981	43.79000	75.17000	86.24000	104.10000	188.50000
area_mean	569.0	654.889104	351.914129	143.50000	420.30000	551.10000	782.70000	2501.00000
smoothness_mean	569.0	0.096360	0.014064	0.05263	0.08637	0.09587	0.10530	0.16340
compactness_mean	569.0	0.104341	0.052813	0.01938	0.06492	0.09263	0.13040	0.34540
concavity_mean	569.0	0.088799	0.079720	0.00000	0.02956	0.06154	0.13070	0.42680
concave points_mean	569.0	0.048919	0.038803	0.00000	0.02031	0.03350	0.07400	0.20120
symmetry_mean	569.0	0.181162	0.027414	0.10600	0.16190	0.17920	0.19570	0.30400
fractal_dimension_mean	569.0	0.062798	0.007060	0.04996	0.05770	0.06154	0.06612	0.09744

	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean
diagnosis	1.000000	0.745217	0.419123	0.759006	0.752786	0.366931	0.608494	0.727168	0.788099	0.334536	-0.006801
radius_mean	0.745217	1.000000	0.332760	0.997692	0.993891	0.167672	0.502741	0.674539	0.811833	0.146763	-0.319056
texture_mean	0.419123	0.332760	1.000000	0.339588	0.341552	-0.015997	0.241666	0.318654	0.301509	0.079586	-0.065946
perimeter_mean	0.759006	0.997692	0.339588	1.000000	0.991861	0.204990	0.553698	0.714624	0.841871	0.181325	-0.266586
area_mean	0.752786	0.993891	0.341552	0.991861	1.000000	0.167400	0.498665	0.680126	0.814133	0.155152	-0.303524
smoothness_mean	0.366931	0.167672	-0.015997	0.204990	0.167400	1.000000	0.661938	0.526544	0.560961	0.557597	0.586706
compactness_mean	0.608494	0.502741	0.241666	0.553698	0.498665	0.661938	1.000000	0.889882	0.832541	0.588842	0.557003
concavity_mean	0.727168	0.674539	0.318654	0.714624	0.680126	0.526544	0.889882	1.000000	0.928382	0.489029	0.325513
concave points_mean	0.788099	0.811833	0.301509	0.841871	0.814133	0.560961	0.832541	0.928382	1.000000	0.460386	0.170028
symmetry_mean	0.334536	0.146763	0.079586	0.181325	0.155152	0.557597	0.588842	0.489029	0.460386	1.000000	0.471760
fractal_dimension_mean	-0.006801	-0.319056	-0.065946	-0.266586	-0.303524	0.586706	0.557003	0.325513	0.170028	0.471760	1.000000

Importing libraries¶

Exploratory Data Analysis¶

2. Check missing values, outliers and statistics¶

3. Check distributions and coorelations¶

k Nearet Neighbor (kNN)¶

Answer the following questions:¶

1. What is the maximum accuracy you can achieve with the least amount of features?¶

2. How does k affect accuracy?¶

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean
0	842302	1	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	0.07871
1	842517	1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	0.05667
2	84300903	1	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	0.05999
3	84348301	1	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	0.09744
4	84358402	1	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	0.05883
...	...	...	...	...	...	...	...	...	...	...	...	...
564	926424	1	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	0.05623
565	926682	1	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	0.05533
566	926954	1	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	0.05648
567	927241	1	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	0.07016
568	92751	0	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	0.05884